


SINGAPORE - Generative AI methodologies have been around for more than 30 years. The famous CoverStory methodology co-authored by the Massachusetts Institute of Technology’s John D.C. Little in 1990 stands out as a breakthrough application for business.
That method added an authoring layer on top of an algorithm layer that leveraged scanner data and was created to produce news for marketing managers by algorithmically discovering competitor actions and sending memos regarding ideas of how to respond, among other things.
From such early applications came companies such as Chicago-based Narrative Science, which created financial news for businesses using natural language generation. Research reports, weather reports, crossword puzzle books, games, short videos, and a slew of other formats have also used generative methodologies to create content.
So, what is new? This is where the jargon gets thick. Artificial intelligence (AI) folks tend to refer to cognitive computing, symbolic AI, rule-based algorithms, machine learning, and deep learning (DL). The layperson might see these as interchangeable, but they are not. The battle across methods - old school, new school, or combinations of these - is where regulators, politicians and the general public must focus their attention. This is not a trivial matter, especially given the lack of understanding of the complicated systems under development.
ChatGPT versus TotoGEO
One may be better served framing this debate with a simple comparison of ChatGPT’s output to that of the TotoGEO AI of France’s Institut Européen d'Administration des Affaires (INSEAD) lab. Both methods generate text, hence the name generative AI. When I asked ChatGPT to write a Wikipedia article about Tinwarrole, here
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